Aspect-Based Sentiment Analysis (ABSA) aims to determine the sentiment polarity of certain aspect words in a sentence.Recently, it is a popular approach to fuse the sentences' syntactic information via the dependency tree into the graph neural network.However, how to efficiently utilize the obtained syntactic information is still a challenging problem of this kind of approach.Therefore, this paper proposes a novel Aspect-level Sentiment Analysis model based on Convolutional network with Dependency Tree, named ASAC-DT in short.First, the attention mechanism is utilized to obtain the attention score of the sentence and the aspect word respectively, to improve the connection of the words related to the aspect word in the sentence.Afterwards, by relying on the syntactic information obtained from the dependency tree, the connections of words that are not related to the aspect words are reduced.Finally, the feature information most relevant to the aspect words in the proposed model is extracted through the graph convolutional neural network and the interactive network.Through extensive experimental baselines the proposed ASAC-DT model shows effectiveness in aspect-level sentiment classification and outperforms baselines in accuracy.

Summary Delta compression, which is efficient in removing repeated string among similar chunks, can be used as a complement to data deduplication in backup storage for extra space savings. The process of detecting similar candidates to use as the base for delta compression is called resemblance detection. Several indexes are required for resemblance detection. Maintaining them in RAM would limit the system scalability and increase system cost. Storing them on the disk suffers from low throughput due to poor random I/O performance of the disk. In this article, we present the history‐aware resemblance detection (HARD), a cost‐efficient resemblance detection approach that captures most of the similar chunks with a limited memory footprint. HARD is based on the observation that, for chunks in a backup, most of their similar chunks can be found in the most recent backups. HARD thus only indexes super‐features in the most recent backups for resemblance detection to reduce the memory footprint of resemblance indexes while captures most of the potential similar chunks for delta compression. Experimental results based on three real‐world datasets show that HARD achieves higher compression than the state‐of‐the‐art approach.

The minimum connected dominating set (MCDS) problem consists of selecting a minimum set of vertices from an undirected graph, such that each vertex not in this set is adjacent to at least one of the vertices in it, and the subgraph induced by this vertex set is connected. This paper presents a fast vertex weighting (FVW) algorithm for solving the MCDS problem, which integrates several distinguishing features, such as a vertex weighting-based local search with tabu and perturbation strategies to help the search to jump out of the local optima, as well as a search space reduction strategy to improve the search efficiency. Computational experiments on four sets of 112 commonly used public benchmark instances, as well as 15 newly introduced sparse instances, show that FVW is highly competitive compared with the state-of-the-art algorithms in the literature despite its simplicity. FVW improves the previous best-known results for 20 large public benchmark instances while matching the best-known results for all but 2 of the remaining ones. Several ingredients of FVW are investigated to demonstrate the importance of the proposed ideas and techniques. Summary of Contribution: As a challenging classical NP-hard problem, the minimum connected dominating set (MCDS) problem has been studied for decades in the areas of both operations research and computer science, although there does not exist an exact polynomial algorithm for solving it. Thus, the new breakthrough on this classical NP-hard problem in terms of the computational results on classical benchmark instances is significant. This paper presents a new fast vertex weighting local search for solving the MCDS problem. Computational experiments on four sets of 112 commonly used public benchmark instances show that fast vertex weighting (FVW) is able to improve the previous best-known results for 20 large instances while matching the best-known results for all but 2 of the remaining instances. Several ingredients of FVW are also investigated to demonstrate the importance of the proposed ideas and techniques.

Data deduplication has become a standard feature in most storage backup systems to reduce storage costs. In real-world deduplication-based backup products, small files are grouped into larger packed files prior to deduplication. For each file, the grouping entails a backup product inserting a metadata block immediately before the file contents. Since the contents of these metadata blocks vary with every backup, different backup streams of the packed files from the same or highly similar small files will contain chunks that are considered mostly unique by conventional deduplication. That is, most of the contents among these unique chunks in different backups are identical, except for metadata blocks. Delta compression is able to remove those redundancy but cannot be applied to backup storage because the extra I/Os required to retrieve the base chunks significantly decrease backup throughput. If there are many grouped small files in the backup datasets, some duplicate chunks, called persistent fragmented chunks (PFCs), may be rewritten repeatedly. We observe that PFCs are often surrounded by substantial unique chunks containing metadata blocks. In this paper, we propose a PFC-inspired delta compression scheme to efficiently perform delta compression for unique chunks surrounding identical PFCs.In the process of deduplication, containers holding previous copies of the chunks being considered for storage will be accessed for prefetching metadata to accelerate the detection of duplicates. The main idea behind our scheme is to identify containers holding PFCs and prefetch chunks in those containers by piggybacking on the reads for prefetching metadata when they are accessed during deduplication. Base chunks for delta compression are then detected from the prefetched chunks, thus eliminating extra I/Os for retrieving the base chunks. Experimental results show that PFC-inspired delta compression attains additional data reduction by about 2x on top of data deduplications and accelerates the restore speed by 8.6%-49.3%, while moderately sacrificing the backup throughput by 0.5%-11.9%.

Data deduplication, though being efficient for redundancy elimination in storage systems, introduces chunk fragmentation which severely decreases restore performance. Rewriting algorithms are proposed to reduce the chunk fragmentation. Typically, the backup software aggregates files into larger “tar” type files for storage. We observe that, in tar type datasets, a large number of Persistent Fragmented Chunks (PFCs) are repeatedly rewritten by state-of-the-art rewriting algorithms in every backup, which severely impacts restore performance. We found that the existence of PFCs is due to the traditional strategy of storing PFCs along with other chunks in the containers to preserve the stream locality, rendering them always stored in the containers with low utilization. We propose DePFC to reduce PFCs. DePFC identifies and removes PFCs from the containers preserving the stream locality, and groups them together, to increase the utilization of containers holding them for the subsequent backup, thus preventing them from being rewritten again. We further propose an FC Buffer to avoid mistaken rewrites of PFCs and grouping PFCs that cause restore cache thrashing together. Experimental results demonstrate that DePFC improves restore performance of state-of-the-art rewriting algorithms by 44.24-89.42 percent, while attaining comparable deduplication efficiency, and FC Buffer further improves restore performance.

Data deduplication, though being efficient in removing duplicate chunks, introduces chunk fragmentation which decreases restore performance. Rewriting algorithms are proposed to reduce the chunk fragmentation. Delta compression is often used as a complement for data deduplication to further improve storage efficiency. We observe that delta compression introduces a new type of chunk fragmentation stemming from improper delta compression for chunks of which the base chunks are fragmented. The new type of chunk fragmentation severely decreases restore performance and cannot be addressed by existing rewriting algorithms. To address this problem, we propose SDC, a scheme performing post-deduplication delta compression only for the chunks of which the bases can be directly found in the restore cache to eliminate additional disk reads for base chunks, thus avoiding the new type of chunk fragmentation. In addition, self-referenced chunks can be fragmented, which decrease restore performance, and these fragmented chunks can serve as bases to decrease the restore performance repeatedly. We propose a hybrid rewriting scheme for SDC to rewrite such fragmented chunks. Experimental results show that SDC improves the restore performance of the approach that directly performs delta compression after data deduplication by 2.9-16.9x, and achieves more than 95 percent of its compression gains.

The minimum connected dominating set problem (MCDSP) has become increasingly important in recent years due to its applicability to mobile ad hoc networks and sensor grids. This paper presents a restricted swap-based neighborhood (RSN) tailored for solving MCDSP. This novel neighborhood structure is embedded into tabu Search (TS) and a perturbation mechanism is employed to enhance diversification. The proposed RSN-TS algorithm is tested on four sets of public benchmark instances widely used in the literature. The results demonstrate the efficacy of the proposed algorithm in terms of both solution quality and computational efficiency. In particular, the RSN-TS algorithm was able to improve the best known results on 41 out of the 97 problem instances while matching the best known results on all the remaining 56 instances. Furthermore, the article analyzes some key features of the proposed approach in order to identify its critical success factors. © 2016 Wiley Periodicals, Inc. NETWORKS, Vol. 69(2), 222–236 2017